Overview

Dataset statistics

Number of variables19
Number of observations8636
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory190.6 B

Variable types

Numeric18
Categorical1

Alerts

balance is highly overall correlated with balance_freq and 5 other fieldsHigh correlation
balance_freq is highly overall correlated with balance and 1 other fieldsHigh correlation
purchases is highly overall correlated with one_purchases and 5 other fieldsHigh correlation
one_purchases is highly overall correlated with purchases and 2 other fieldsHigh correlation
install_purchases is highly overall correlated with purchases and 3 other fieldsHigh correlation
cash_adv is highly overall correlated with balance and 2 other fieldsHigh correlation
purchases_freq is highly overall correlated with purchases and 3 other fieldsHigh correlation
one_purchases_freq is highly overall correlated with purchases and 2 other fieldsHigh correlation
purchases_install_freq is highly overall correlated with purchases and 3 other fieldsHigh correlation
cash_adv_freq is highly overall correlated with balance and 2 other fieldsHigh correlation
cash_adv_trx is highly overall correlated with balance and 2 other fieldsHigh correlation
purchases_trx is highly overall correlated with purchases and 5 other fieldsHigh correlation
payments is highly overall correlated with outliersHigh correlation
min_pay is highly overall correlated with balance and 1 other fieldsHigh correlation
prc_full_pay is highly overall correlated with balanceHigh correlation
outliers is highly overall correlated with paymentsHigh correlation
outliers is highly imbalanced (53.1%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
payments has unique valuesUnique
purchases has 1967 (22.8%) zerosZeros
one_purchases has 4113 (47.6%) zerosZeros
install_purchases has 3747 (43.4%) zerosZeros
cash_adv has 4431 (51.3%) zerosZeros
purchases_freq has 1966 (22.8%) zerosZeros
one_purchases_freq has 4113 (47.6%) zerosZeros
purchases_install_freq has 3746 (43.4%) zerosZeros
cash_adv_freq has 4431 (51.3%) zerosZeros
cash_adv_trx has 4431 (51.3%) zerosZeros
purchases_trx has 1967 (22.8%) zerosZeros
prc_full_pay has 5589 (64.7%) zerosZeros

Reproduction

Analysis started2023-01-26 18:40:55.682759
Analysis finished2023-01-26 18:41:47.076482
Duration51.39 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14602.541
Minimum10001
Maximum19190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:47.202033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10487.75
Q112337.75
median14592.5
Q316885.25
95-th percentile18719.25
Maximum19190
Range9189
Interquartile range (IQR)4547.5

Descriptive statistics

Standard deviation2632.7728
Coefficient of variation (CV)0.18029552
Kurtosis-1.1887838
Mean14602.541
Median Absolute Deviation (MAD)2273.5
Skewness0.002493971
Sum1.2610754 × 108
Variance6931492.4
MonotonicityStrictly increasing
2023-01-26T10:41:47.399545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 1
 
< 0.1%
16111 1
 
< 0.1%
16125 1
 
< 0.1%
16124 1
 
< 0.1%
16123 1
 
< 0.1%
16122 1
 
< 0.1%
16121 1
 
< 0.1%
16120 1
 
< 0.1%
16119 1
 
< 0.1%
16118 1
 
< 0.1%
Other values (8626) 8626
99.9%
ValueCountFrequency (%)
10001 1
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10005 1
< 0.1%
10006 1
< 0.1%
10007 1
< 0.1%
10008 1
< 0.1%
10009 1
< 0.1%
10010 1
< 0.1%
10011 1
< 0.1%
ValueCountFrequency (%)
19190 1
< 0.1%
19189 1
< 0.1%
19188 1
< 0.1%
19186 1
< 0.1%
19184 1
< 0.1%
19183 1
< 0.1%
19182 1
< 0.1%
19181 1
< 0.1%
19180 1
< 0.1%
19179 1
< 0.1%

balance
Real number (ℝ)

Distinct8631
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1601.2249
Minimum0
Maximum19043.139
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:47.577587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.209472
Q1148.09519
median916.85546
Q32105.1959
95-th percentile5936.6356
Maximum19043.139
Range19043.139
Interquartile range (IQR)1957.1007

Descriptive statistics

Standard deviation2095.5713
Coefficient of variation (CV)1.3087302
Kurtosis7.553876
Mean1601.2249
Median Absolute Deviation (MAD)825.60645
Skewness2.3742542
Sum13828178
Variance4391419.1
MonotonicityNot monotonic
2023-01-26T10:41:47.765922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.1%
40.900749 1
 
< 0.1%
1253.188317 1
 
< 0.1%
394.643543 1
 
< 0.1%
617.413726 1
 
< 0.1%
765.109593 1
 
< 0.1%
2583.247881 1
 
< 0.1%
1146.669364 1
 
< 0.1%
757.470201 1
 
< 0.1%
5058.299635 1
 
< 0.1%
Other values (8621) 8621
99.8%
ValueCountFrequency (%)
0 6
0.1%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.064811 1
 
< 0.1%
0.065402 1
 
< 0.1%
0.074724 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

balance_freq
Real number (ℝ)

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89503511
Minimum0
Maximum1
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:47.965444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.363636
Q10.909091
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.090909

Descriptive statistics

Standard deviation0.20769688
Coefficient of variation (CV)0.23205444
Kurtosis3.3695861
Mean0.89503511
Median Absolute Deviation (MAD)0
Skewness-2.0841615
Sum7729.5232
Variance0.043137992
MonotonicityNot monotonic
2023-01-26T10:41:48.140481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 6130
71.0%
0.909091 406
 
4.7%
0.818182 274
 
3.2%
0.727273 220
 
2.5%
0.545455 217
 
2.5%
0.636364 202
 
2.3%
0.454545 170
 
2.0%
0.363636 167
 
1.9%
0.272727 141
 
1.6%
0.181818 117
 
1.4%
Other values (32) 592
 
6.9%
ValueCountFrequency (%)
0 6
 
0.1%
0.090909 25
 
0.3%
0.1 2
 
< 0.1%
0.125 2
 
< 0.1%
0.142857 1
 
< 0.1%
0.166667 1
 
< 0.1%
0.181818 117
1.4%
0.2 7
 
0.1%
0.222222 2
 
< 0.1%
0.25 5
 
0.1%
ValueCountFrequency (%)
1 6130
71.0%
0.909091 406
 
4.7%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.7%
0.857143 50
 
0.6%
0.833333 59
 
0.7%
0.818182 274
 
3.2%
0.8 20
 
0.2%
0.777778 21
 
0.2%

purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6015
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1025.4339
Minimum0
Maximum49039.57
Zeros1967
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:48.315096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.3675
median375.405
Q31145.98
95-th percentile4060.0925
Maximum49039.57
Range49039.57
Interquartile range (IQR)1102.6125

Descriptive statistics

Standard deviation2167.108
Coefficient of variation (CV)2.1133571
Kurtosis108.67768
Mean1025.4339
Median Absolute Deviation (MAD)375.405
Skewness8.055789
Sum8855646.9
Variance4696357
MonotonicityNot monotonic
2023-01-26T10:41:48.498870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1967
 
22.8%
45.65 25
 
0.3%
150 15
 
0.2%
60 13
 
0.2%
200 12
 
0.1%
450 12
 
0.1%
100 12
 
0.1%
600 10
 
0.1%
70 10
 
0.1%
1000 9
 
0.1%
Other values (6005) 6551
75.9%
ValueCountFrequency (%)
0 1967
22.8%
0.01 3
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
4.99 1
 
< 0.1%
6.9 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

one_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3922
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604.90144
Minimum0
Maximum40761.25
Zeros4113
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:48.685821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44.995
Q3599.1
95-th percentile2728.3725
Maximum40761.25
Range40761.25
Interquartile range (IQR)599.1

Descriptive statistics

Standard deviation1684.3078
Coefficient of variation (CV)2.7844335
Kurtosis160.12131
Mean604.90144
Median Absolute Deviation (MAD)44.995
Skewness9.9357759
Sum5223928.8
Variance2836892.8
MonotonicityNot monotonic
2023-01-26T10:41:48.868664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4113
47.6%
45.65 43
 
0.5%
50 16
 
0.2%
200 15
 
0.2%
70 12
 
0.1%
150 12
 
0.1%
1000 12
 
0.1%
100 12
 
0.1%
250 11
 
0.1%
60 10
 
0.1%
Other values (3912) 4380
50.7%
ValueCountFrequency (%)
0 4113
47.6%
0.01 6
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
1 4
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

install_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4341
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.84353
Minimum0
Maximum22500
Zeros3747
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:49.075900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.785
Q3484.1475
95-th percentile1800
Maximum22500
Range22500
Interquartile range (IQR)484.1475

Descriptive statistics

Standard deviation917.24518
Coefficient of variation (CV)2.1795397
Kurtosis94.193373
Mean420.84353
Median Absolute Deviation (MAD)94.785
Skewness7.2161333
Sum3634404.8
Variance841338.72
MonotonicityNot monotonic
2023-01-26T10:41:49.242383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3747
43.4%
100 14
 
0.2%
200 13
 
0.2%
125 11
 
0.1%
150 11
 
0.1%
300 10
 
0.1%
75 9
 
0.1%
450 8
 
0.1%
500 8
 
0.1%
270 7
 
0.1%
Other values (4331) 4798
55.6%
ValueCountFrequency (%)
0 3747
43.4%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
9.68 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

cash_adv
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4206
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean994.17552
Minimum0
Maximum47137.212
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:49.413185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31132.3855
95-th percentile4721.4155
Maximum47137.212
Range47137.212
Interquartile range (IQR)1132.3855

Descriptive statistics

Standard deviation2121.4583
Coefficient of variation (CV)2.1338871
Kurtosis52.143523
Mean994.17552
Median Absolute Deviation (MAD)0
Skewness5.1396286
Sum8585699.8
Variance4500585.3
MonotonicityNot monotonic
2023-01-26T10:41:49.580276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4431
51.3%
2411.584248 1
 
< 0.1%
92.6579 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
Other values (4196) 4196
48.6%
ValueCountFrequency (%)
0 4431
51.3%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.496
Minimum0
Maximum1
Zeros1966
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:49.752304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40127264
Coefficient of variation (CV)0.80901742
Kurtosis-1.6380013
Mean0.496
Median Absolute Deviation (MAD)0.416667
Skewness0.033041216
Sum4283.456
Variance0.16101973
MonotonicityNot monotonic
2023-01-26T10:41:49.920973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2126
24.6%
0 1966
22.8%
0.083333 622
 
7.2%
0.916667 391
 
4.5%
0.5 390
 
4.5%
0.833333 367
 
4.2%
0.166667 367
 
4.2%
0.333333 350
 
4.1%
0.25 328
 
3.8%
0.583333 309
 
3.6%
Other values (37) 1420
16.4%
ValueCountFrequency (%)
0 1966
22.8%
0.083333 622
 
7.2%
0.090909 41
 
0.5%
0.1 23
 
0.3%
0.111111 16
 
0.2%
0.125 25
 
0.3%
0.142857 22
 
0.3%
0.166667 367
 
4.2%
0.181818 15
 
0.2%
0.2 17
 
0.2%
ValueCountFrequency (%)
1 2126
24.6%
0.916667 391
 
4.5%
0.909091 28
 
0.3%
0.9 23
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 23
 
0.3%
0.833333 367
 
4.2%
0.818182 20
 
0.2%
0.8 9
 
0.1%

one_purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20590874
Minimum0
Maximum1
Zeros4113
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:50.105002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.333333
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.333333

Descriptive statistics

Standard deviation0.30005361
Coefficient of variation (CV)1.4572165
Kurtosis1.0582057
Mean0.20590874
Median Absolute Deviation (MAD)0.083333
Skewness1.5042342
Sum1778.2279
Variance0.090032167
MonotonicityNot monotonic
2023-01-26T10:41:50.291775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4113
47.6%
0.083333 1057
 
12.2%
0.166667 576
 
6.7%
1 469
 
5.4%
0.25 408
 
4.7%
0.333333 346
 
4.0%
0.416667 243
 
2.8%
0.5 232
 
2.7%
0.583333 197
 
2.3%
0.666667 167
 
1.9%
Other values (37) 828
 
9.6%
ValueCountFrequency (%)
0 4113
47.6%
0.083333 1057
 
12.2%
0.090909 54
 
0.6%
0.1 36
 
0.4%
0.111111 24
 
0.3%
0.125 35
 
0.4%
0.142857 33
 
0.4%
0.166667 576
 
6.7%
0.181818 33
 
0.4%
0.2 26
 
0.3%
ValueCountFrequency (%)
1 469
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 115
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

purchases_install_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36882035
Minimum0
Maximum1
Zeros3746
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:50.463143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39809294
Coefficient of variation (CV)1.0793682
Kurtosis-1.4192794
Mean0.36882035
Median Absolute Deviation (MAD)0.166667
Skewness0.48775295
Sum3185.1325
Variance0.15847799
MonotonicityNot monotonic
2023-01-26T10:41:50.637096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3746
43.4%
1 1297
 
15.0%
0.416667 381
 
4.4%
0.916667 340
 
3.9%
0.833333 304
 
3.5%
0.5 303
 
3.5%
0.166667 296
 
3.4%
0.666667 290
 
3.4%
0.75 284
 
3.3%
0.083333 249
 
2.9%
Other values (37) 1146
 
13.3%
ValueCountFrequency (%)
0 3746
43.4%
0.083333 249
 
2.9%
0.090909 10
 
0.1%
0.1 5
 
0.1%
0.111111 8
 
0.1%
0.125 4
 
< 0.1%
0.142857 5
 
0.1%
0.166667 296
 
3.4%
0.181818 14
 
0.2%
0.2 8
 
0.1%
ValueCountFrequency (%)
1 1297
15.0%
0.916667 340
 
3.9%
0.909091 25
 
0.3%
0.9 18
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 29
 
0.3%
0.833333 304
 
3.5%
0.818182 20
 
0.2%
0.8 17
 
0.2%

cash_adv_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1376042
Minimum0
Maximum1.5
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:50.814214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.20179143
Coefficient of variation (CV)1.4664627
Kurtosis3.1842333
Mean0.1376042
Median Absolute Deviation (MAD)0
Skewness1.795915
Sum1188.3499
Variance0.040719781
MonotonicityNot monotonic
2023-01-26T10:41:50.982468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4431
51.3%
0.083333 980
 
11.3%
0.166667 730
 
8.5%
0.25 573
 
6.6%
0.333333 434
 
5.0%
0.416667 272
 
3.1%
0.5 209
 
2.4%
0.583333 142
 
1.6%
0.666667 124
 
1.4%
0.090909 66
 
0.8%
Other values (44) 675
 
7.8%
ValueCountFrequency (%)
0 4431
51.3%
0.083333 980
 
11.3%
0.090909 66
 
0.8%
0.1 36
 
0.4%
0.111111 23
 
0.3%
0.125 43
 
0.5%
0.142857 43
 
0.5%
0.166667 730
 
8.5%
0.181818 41
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 24
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

cash_adv_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3139185
Minimum0
Maximum123
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:51.158894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.9125061
Coefficient of variation (CV)2.0859011
Kurtosis60.428523
Mean3.3139185
Median Absolute Deviation (MAD)0
Skewness5.6733268
Sum28619
Variance47.782741
MonotonicityNot monotonic
2023-01-26T10:41:51.338091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4431
51.3%
1 839
 
9.7%
2 602
 
7.0%
3 429
 
5.0%
4 374
 
4.3%
5 300
 
3.5%
6 241
 
2.8%
7 202
 
2.3%
8 169
 
2.0%
10 147
 
1.7%
Other values (55) 902
 
10.4%
ValueCountFrequency (%)
0 4431
51.3%
1 839
 
9.7%
2 602
 
7.0%
3 429
 
5.0%
4 374
 
4.3%
5 300
 
3.5%
6 241
 
2.8%
7 202
 
2.3%
8 169
 
2.0%
9 108
 
1.3%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

purchases_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.033233
Minimum0
Maximum358
Zeros1967
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:51.520524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile59
Maximum358
Range358
Interquartile range (IQR)17

Descriptive statistics

Standard deviation25.180468
Coefficient of variation (CV)1.6749869
Kurtosis33.952279
Mean15.033233
Median Absolute Deviation (MAD)7
Skewness4.5784185
Sum129827
Variance634.05599
MonotonicityNot monotonic
2023-01-26T10:41:51.706522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1967
22.8%
1 606
 
7.0%
12 537
 
6.2%
2 345
 
4.0%
6 340
 
3.9%
3 294
 
3.4%
4 277
 
3.2%
7 265
 
3.1%
8 263
 
3.0%
5 254
 
2.9%
Other values (163) 3488
40.4%
ValueCountFrequency (%)
0 1967
22.8%
1 606
 
7.0%
2 345
 
4.0%
3 294
 
3.4%
4 277
 
3.2%
5 254
 
2.9%
6 340
 
3.9%
7 265
 
3.1%
8 263
 
3.0%
9 240
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

credit_limit
Real number (ℝ)

Distinct203
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4522.091
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:51.886935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3659.2404
Coefficient of variation (CV)0.80919211
Kurtosis2.7734731
Mean4522.091
Median Absolute Deviation (MAD)1800
Skewness1.507019
Sum39052778
Variance13390040
MonotonicityNot monotonic
2023-01-26T10:41:52.055221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 752
 
8.7%
1500 695
 
8.0%
1200 597
 
6.9%
1000 596
 
6.9%
2500 584
 
6.8%
4000 471
 
5.5%
6000 449
 
5.2%
5000 370
 
4.3%
2000 364
 
4.2%
7500 273
 
3.2%
Other values (193) 3485
40.4%
ValueCountFrequency (%)
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 112
1.3%
600 21
 
0.2%
650 1
 
< 0.1%
700 20
 
0.2%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

payments
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784.4781
Minimum0.049513
Maximum50721.483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:52.241904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.049513
5-th percentile143.55957
Q1418.55924
median896.6757
Q31951.1421
95-th percentile6152.3187
Maximum50721.483
Range50721.434
Interquartile range (IQR)1532.5829

Descriptive statistics

Standard deviation2909.8101
Coefficient of variation (CV)1.6306225
Kurtosis54.270814
Mean1784.4781
Median Absolute Deviation (MAD)592.62252
Skewness5.8730486
Sum15410753
Variance8466994.8
MonotonicityNot monotonic
2023-01-26T10:41:52.435856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201.802084 1
 
< 0.1%
6372.619037 1
 
< 0.1%
162.949236 1
 
< 0.1%
164.403739 1
 
< 0.1%
1679.00486 1
 
< 0.1%
209.392729 1
 
< 0.1%
1014.549633 1
 
< 0.1%
272.517748 1
 
< 0.1%
32.924384 1
 
< 0.1%
1899.738286 1
 
< 0.1%
Other values (8626) 8626
99.9%
ValueCountFrequency (%)
0.049513 1
< 0.1%
0.056466 1
< 0.1%
3.500505 1
< 0.1%
4.523555 1
< 0.1%
4.841543 1
< 0.1%
9.533313 1
< 0.1%
12.773144 1
< 0.1%
14.500688 1
< 0.1%
16.385421 1
< 0.1%
18.125527 1
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

min_pay
Real number (ℝ)

Distinct8635
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.30494
Minimum0.019163
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:53.307804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.358542
Q1169.16355
median312.45229
Q3825.49646
95-th percentile2766.5939
Maximum76406.208
Range76406.188
Interquartile range (IQR)656.33292

Descriptive statistics

Standard deviation2372.5664
Coefficient of variation (CV)2.745057
Kurtosis283.96304
Mean864.30494
Median Absolute Deviation (MAD)190.37279
Skewness13.622193
Sum7464137.5
Variance5629071.1
MonotonicityNot monotonic
2023-01-26T10:41:53.487930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.351881 2
 
< 0.1%
342.28649 1
 
< 0.1%
184.464721 1
 
< 0.1%
276.486072 1
 
< 0.1%
309.140865 1
 
< 0.1%
354.281114 1
 
< 0.1%
216.090433 1
 
< 0.1%
277.546713 1
 
< 0.1%
150.317143 1
 
< 0.1%
1600.26917 1
 
< 0.1%
Other values (8625) 8625
99.9%
ValueCountFrequency (%)
0.019163 1
< 0.1%
0.037744 1
< 0.1%
0.05588 1
< 0.1%
0.059481 1
< 0.1%
0.117036 1
< 0.1%
0.261984 1
< 0.1%
0.311953 1
< 0.1%
0.319475 1
< 0.1%
1.113027 1
< 0.1%
1.334075 1
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

prc_full_pay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15930362
Minimum0
Maximum1
Zeros5589
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:53.672802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.166667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.166667

Descriptive statistics

Standard deviation0.29627091
Coefficient of variation (CV)1.8597876
Kurtosis2.2015985
Mean0.15930362
Median Absolute Deviation (MAD)0
Skewness1.8860271
Sum1375.7461
Variance0.087776452
MonotonicityNot monotonic
2023-01-26T10:41:53.844648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5589
64.7%
1 488
 
5.7%
0.083333 426
 
4.9%
0.166667 166
 
1.9%
0.5 156
 
1.8%
0.25 156
 
1.8%
0.090909 153
 
1.8%
0.333333 134
 
1.6%
0.1 94
 
1.1%
0.2 83
 
1.0%
Other values (37) 1191
 
13.8%
ValueCountFrequency (%)
0 5589
64.7%
0.083333 426
 
4.9%
0.090909 153
 
1.8%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.9%
0.2 83
 
1.0%
ValueCountFrequency (%)
1 488
5.7%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

tenure
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.534391
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size134.9 KiB
2023-01-26T10:41:53.981415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3109837
Coefficient of variation (CV)0.11365868
Kurtosis8.1567014
Mean11.534391
Median Absolute Deviation (MAD)0
Skewness-3.0111405
Sum99611
Variance1.7186782
MonotonicityNot monotonic
2023-01-26T10:41:54.090663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7346
85.1%
11 356
 
4.1%
10 226
 
2.6%
6 184
 
2.1%
8 183
 
2.1%
7 177
 
2.0%
9 164
 
1.9%
ValueCountFrequency (%)
6 184
 
2.1%
7 177
 
2.0%
8 183
 
2.1%
9 164
 
1.9%
10 226
 
2.6%
11 356
 
4.1%
12 7346
85.1%
ValueCountFrequency (%)
12 7346
85.1%
11 356
 
4.1%
10 226
 
2.6%
9 164
 
1.9%
8 183
 
2.1%
7 177
 
2.0%
6 184
 
2.1%

outliers
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size134.9 KiB
1
7772 
-1
864 

Length

Max length2
Median length1
Mean length1.1000463
Min length1

Characters and Unicode

Total characters9500
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7772
90.0%
-1 864
 
10.0%

Length

2023-01-26T10:41:54.226297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T10:41:54.400259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8636
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8636
90.9%
- 864
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8636
90.9%
Dash Punctuation 864
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8636
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8636
90.9%
- 864
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8636
90.9%
- 864
 
9.1%

Interactions

2023-01-26T10:41:43.633185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:57.015960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:59.527071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:02.497296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:05.218095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.959701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.812831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:13.650869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:16.102730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:18.609127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:21.140901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:24.528620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:27.279768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.926384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:32.616520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:35.223756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-26T10:41:36.731535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:39.948479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:42.578345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:45.409599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:58.695497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:01.339990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:04.274216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.029667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:09.868594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:12.819017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.262380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:17.755984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.288447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:23.477426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:26.309852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.024385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:31.674907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:34.366684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:36.882163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.101665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:42.726125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:45.573737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:58.832720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:01.489616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:04.447895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.174232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.021717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:12.958008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.407004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:17.898968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.431057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:23.662298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:26.468473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.179997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:31.849925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:34.514174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:37.585684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.266524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:42.891004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:45.717250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:58.979685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:01.631535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:04.606755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.345773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.169599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:13.095771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.546219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:18.042631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.571934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:23.843582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:26.644302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.328362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:32.001366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:34.655600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:37.728769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.417502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:43.036888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:45.858540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:59.114882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:01.768181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:04.765748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.484956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.315549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:13.224910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.690002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:18.180107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.705828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:24.010318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:26.791103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.464890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:32.150501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:34.790749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:37.861997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.558923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:43.174622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:46.026299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:59.263510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:01.933779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:04.934086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.640909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.483733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:13.377035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.837513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:18.331296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.858171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:24.196188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:26.971364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.640960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:32.313789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:34.941899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:38.013054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.737649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:43.346604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:46.172545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:40:59.392580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:02.351882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:05.073951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:07.796042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:10.652194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:13.511566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:15.967964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:18.468546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:20.994116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:24.346315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:27.131690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:29.779075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:32.456457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:35.078656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:38.147282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:40.885843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T10:41:43.484010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-26T10:41:54.531825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureoutliers
id1.000-0.243-0.121-0.118-0.187-0.022-0.047-0.026-0.1810.019-0.020-0.022-0.081-0.375-0.244-0.2050.059-0.1690.110
balance-0.2431.0000.513-0.0130.130-0.1040.570-0.1630.103-0.1550.5450.551-0.0640.3780.4190.900-0.5320.0560.346
balance_freq-0.1210.5131.0000.1280.1160.1170.1290.1950.1410.1520.1670.1670.1890.0990.1610.502-0.2220.2260.064
purchases-0.118-0.0130.1281.0000.7530.710-0.3870.7940.6940.609-0.395-0.3880.8860.2630.398-0.0080.2330.1290.493
one_purchases-0.1870.1300.1160.7531.0000.208-0.1900.4270.9520.125-0.185-0.1810.5960.3060.3700.0700.0430.0960.396
install_purchases-0.022-0.1040.1170.7100.2081.000-0.3580.7860.1910.922-0.369-0.3600.7840.1270.234-0.0520.2730.1200.342
cash_adv-0.0470.5700.129-0.387-0.190-0.3581.000-0.454-0.194-0.3780.9400.951-0.4090.1650.2660.482-0.280-0.1150.353
purchases_freq-0.026-0.1630.1950.7940.4270.786-0.4541.0000.4660.853-0.455-0.4480.9210.1050.164-0.1040.2920.0910.159
one_purchases_freq-0.1810.1030.1410.6940.9520.191-0.1940.4661.0000.119-0.182-0.1800.6110.2830.3240.0510.0550.0840.267
purchases_install_freq0.019-0.1550.1520.6090.1250.922-0.3780.8530.1191.000-0.384-0.3760.7800.0500.112-0.0850.2590.1080.106
cash_adv_freq-0.0200.5450.167-0.395-0.185-0.3690.940-0.455-0.182-0.3841.0000.983-0.4100.0900.2020.456-0.303-0.1330.325
cash_adv_trx-0.0220.5510.167-0.388-0.181-0.3600.951-0.448-0.180-0.3760.9831.000-0.4010.0990.2160.472-0.297-0.1000.332
purchases_trx-0.081-0.0640.1890.8860.5960.784-0.4090.9210.6110.780-0.410-0.4011.0000.1930.279-0.0250.2470.1630.401
credit_limit-0.3750.3780.0990.2630.3060.1270.1650.1050.2830.0500.0900.0990.1931.0000.4700.2640.0170.1680.355
payments-0.2440.4190.1610.3980.3700.2340.2660.1640.3240.1120.2020.2160.2790.4701.0000.3680.1590.2040.505
min_pay-0.2050.9000.502-0.0080.070-0.0520.482-0.1040.051-0.0850.4560.472-0.0250.2640.3681.000-0.4790.1360.120
prc_full_pay0.059-0.532-0.2220.2330.0430.273-0.2800.2920.0550.259-0.303-0.2970.2470.0170.159-0.4791.0000.0130.173
tenure-0.1690.0560.2260.1290.0960.120-0.1150.0910.0840.108-0.133-0.1000.1630.1680.2040.1360.0131.0000.212
outliers0.1100.3460.0640.4930.3960.3420.3530.1590.2670.1060.3250.3320.4010.3550.5050.1200.1730.2121.000

Missing values

2023-01-26T10:41:46.432152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-26T10:41:46.868417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureoutliers
01000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.00021000.0201.802084139.5097870.000000121
1100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.25407000.04103.0325971072.3402170.222222121
2100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.000127500.0622.066742627.2847870.000000121
410005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.00011200.0678.334763244.7912370.000000121
5100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.00081800.01400.0577702407.2460350.000000121
610007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.0006413500.06354.314328198.0658941.00000012-1
7100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.000122300.0679.065082532.0339900.000000121
8100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.00057000.0688.278568311.9634090.000000121
910010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.000311000.01164.770591100.3022620.000000121
10100111293.1249391.000000920.120.00920.120.0000001.0000000.0000001.0000000.000121200.01083.3010072172.6977650.000000121
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureoutliers
89381917978.8184070.5000000.000.000.001113.1860780.0000000.0000000.0000000.166667701200.01397.77013121.8211940.3333336-1
893919180728.3525481.000000734.40734.400.00239.8910380.3333330.3333330.0000000.166667221000.072.530037110.9507980.00000061
894019181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.0000006-1
8941191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.0000006-1
89421918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.25000061
8943191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.00000061
89451918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.50000061
89471918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.25000061
89481918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.25000061
894919190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.0000006-1